Black hole accretion preferentially occurs in gas-rich galaxies*
Bibliographic record
Abstract
We have investigated the gas content of a sample of several hundred AGN host galaxies at z < 1 and compared it with a sample of inactive galaxies, matched in bins of stellar mass and redshift. Gas masses have been inferred from the dust masses, obtained by stacked Herschel far-IR and sub-mm data in the GOODS and COSMOS fields, under reasonable assumptions and metallicity scaling relations for the dust-to-gas ratio. We find that AGNs are on average hosted in galaxies much more gas rich than inactive galaxies. In the vast majority of stellar mass bins, the average gas content of AGN hosts is higher than that in inactive galaxies. The difference is up to a factor of 10 higher in low-stellar-mass galaxies, with a significance of 6.5σ. In almost half of the AGN sample, the gas content is three times higher than that in the control sample of inactive galaxies. Our result strongly suggests that the probability of having an AGN activated is simply driven by the amount of gas in the host galaxy; this can be explained in simple terms of statistical probability of having a gas cloud falling into the gravitational potential of the black hole. The increased probability of an AGN being hosted by a star-forming galaxy, identified by previous works, may be a consequence of the relationship between gas content and AGN activity, found in this paper, combined with the Schmidt–Kennicutt law for star formation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".